A new alternative to high-frequency trading software

What is high frequency trading (HFT)?

For a while, it seems that high frequency trading (HFT) will completely take over the market. According to data from the global investment firm Franklin Templeton in 2019, since the Global Financial Crisis (GFC) ten years ago, high-frequency trading has accounted for about half of the trading volume of the US stock market each year.

This may herald the steady speed of high-frequency trading software after its peak usage in 2009, when high-frequency traders moved about 3.25 billion shares every day. According to Bloomberg News, in 2012 this figure was 1.6 billion per day. The report pointed out that at the same time, average profit fell from “approximately one-tenth to one-twentieth per share.”

Using HFT software, powerful computers use complex algorithms to analyze the market and perform ultra-fast transactions, usually large numbers of transactions. HFT requires advanced trading infrastructure, such as powerful computers equipped with high-end hardware, which requires huge amounts of money and cut profits. As competition intensifies, there is no guarantee of success. This article looks at why traders abandon HFT and the alternative strategies they are using now.

Key points

  • In the past ten years, the use of high-frequency trading software (HFT) accounted for about half of the trading volume of the US stock market, indicating that its growth potential is maximized.
  • Over time, HFT software has become more and more popular due to its low error rate; however, the software is expensive and the market has become very crowded.
  • Instead, many HFT alternatives have emerged, including trading strategies based on momentum, news, and social media.

Why high frequency trading (HFT) is losing ground

The establishment and maintenance of the HFT program requires a lot of money. Powerful computer hardware and software require frequent and expensive upgrades, which can erode profits. The market is highly dynamic and it is impossible to copy everything into a computer program. Due to the error of the underlying algorithm, the success rate of HFT is very low.

The world of HFT also includes UHF trading. UHF traders pay for access to exchanges that display quotes earlier than the rest of the market. This extra time advantage puts other market participants at a disadvantage. This situation has led to claims of unfair practices and increasing opposition to HFT.

HFT regulations are also becoming stricter. In 2013, Italy was the first to introduce Special tax on high frequency trading, Followed by similar taxes in France.

The HFT market has also become very crowded. Individuals and professionals are competing with each other for their smartest algorithms. Participants even deploy the HFT algorithm to detect and bid higher than other algorithms. The end result is that high-speed programs compete with each other, further squeezing meager profits.

Due to the aforementioned factors such as increased infrastructure and implementation costs, new taxes and regulations, high-frequency trading profits are shrinking. Former high-frequency traders are turning to alternative trading strategies.

Alternatives to High Frequency Trading (HFT)

The company is shifting to efficient and lower-cost trading strategies that will not lead to stricter supervision.

Momentum trading

Ancient technical analysis indicators based on momentum recognition are one of the popular alternatives to HFT. Momentum trading involves perceiving the direction of price changes that are expected to last for a period of time (ranging from a few minutes to a few months).

Once the computer algorithm perceives a direction, the trader will conduct one or more staggered transactions with large orders. Due to the large number of orders, even small differences in price changes can generate considerable profits over time. Since positions based on momentum trading need to be held for a period of time, there is no need for fast trading within milliseconds or microseconds. This greatly saves infrastructure costs.

News-based automated trading

News drives the market. Exchanges, news organizations, and data providers make a lot of money by selling specialized news feeds to traders. Automated trading based on automatic analysis of news items has gained momentum. Computer programs are now able to read news and take immediate transaction actions in response.

For example, suppose that when the following hypothetical news appears, the stock price of ABC Company is 25.40 US dollars per share: ABC announced a dividend of 20 cents per share, and the ex-dividend date is September 5, 2015. As a result, the stock price will soar to reduce the same amount of dividends (20 cents) to around $25.60. The computer program recognizes keywords such as dividends, dividend amount, and date, and issues real-time transaction orders. It should be programmed to only buy ABC stock when the limited (expected) price rises of $25.60.

This news-based strategy is more effective than HFT because these orders will be sent instantly, mainly on open market quotes, and may be executed at unfavorable prices. In addition to dividends, news-based automated trading is also programmed for project bidding results, company quarterly performance, other company actions (such as stock splits), and foreign exchange rate changes for companies with high overseas risk exposures.

Social media-based transactions

Scanning real-time social media feeds from known sources and trusted market participants is another emerging trend in automated trading. It involves predictive analysis of social media content to make trading decisions and place trading orders.

For example, suppose Paul is a well-known market maker for three known stocks. His dedicated social media feed contains real-time tips about his three stocks. Market participants who believe in Paul’s trading acumen can pay to subscribe to his private real-time feed. His updates are fed into computer algorithms, which analyze and interpret their content, and even update the tone used in the language. In addition to Paul, there are several other trusted participants who share tips on specific stocks. The algorithm aggregates all updates from different trusted sources, analyzes them to make trading decisions, and finally automatically conducts transactions.

Combining social media feed analysis with other inputs such as news analysis and quarterly results can produce a sophisticated but reliable way to perceive the market’s sentiment towards a particular stock’s trend. This kind of predictive analysis is very popular in short-term day trading.

Firmware development mode

Speed ​​is critical to the success of high-frequency trading. The speed depends on the available network and computer configuration (hardware), and the processing power of the application (software). A new concept is to integrate hardware and software to form firmware, which greatly reduces the speed of algorithm processing and decision-making.

This customized firmware is integrated into the hardware and programmed to conduct fast transactions based on the recognized signals. This solves the problem of time delay and dependency when the computer system must run many different applications. This slowdown has become the bottleneck of traditional high-frequency trading.

Bottom line

Too much development by too many participants leads to overcrowding of the market. It limits opportunities and increases operating costs. This trend is leading to a decline in high-frequency trading. However, traders are looking for alternatives to HFT. Some people are returning to traditional trading concepts and low-frequency trading applications, while others are using new analytical tools and techniques.


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